Energy Prediction using Spatiotemporal Pattern Networks

TL;DR

This paper introduces a novel spatiotemporal pattern network (STPN) framework for energy prediction that captures system characteristics and causal dependencies, validated through wind turbine and residential energy case studies.

Contribution

The paper develops a new STPN-based method for energy prediction that incorporates causal dependency quantification and demonstrates its effectiveness on real-world datasets.

Findings

Effective wind power prediction demonstrated.

Improved energy disaggregation performance achieved.

Causal dependencies among subsystems successfully modeled.

Abstract

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamic filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. For quantifying the causal dependency, a mutual information based metric is presented. An energy prediction approach is subsequently proposed based on the STPN framework. For validating the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying the spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring the temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.